9923170071 / 8108094992 info@dimensionless.in
Data Science: What to Expect in 2019

Data Science: What to Expect in 2019

Introduction

2019 looks to be the year of using smarter technology in a smarter way. Three key trends — artificial intelligence systems becoming a serious component in enterprise tools, custom hardware breaking out for special use-cases, and a rethink on data science and its utility — will all combine into a common theme.

In recent years, we’ve seen all manner of jaw-dropping technology, but the emphasis has been very much on what these gadgets and systems can do and how they do it, with much less attention paid to why.

In this blog, we will explore different areas in data science and figure out our expectations in 2019 in them. Areas include machine learning, AR/VR systems, edge computing etc. Let us go through them one by one

Machine Learning/Deep Learning

Businesses are using machine learning to improve all sorts of outcomes, from optimizing operational workflows and increasing customer satisfaction to discovering to a new competitive differentiator. But, now all the hype around AI is settling. Machine learning is not a cool term anymore. Furthermore, organisations are looking for more ways of identifying more options in the form of agent modelling. Apart from this, more adoption of these algorithms looks very feasible now. Adoption will be seen in new and old industries

Healthcare companies are already big users of AI, and this trend will continue. According to Accenture, the AI healthcare market might hit $6.6 billion by 2021, and clinical health AI applications can create $150 billion in annual savings for the U.S. healthcare economy by 2026.

In retail, global spending on AI will grow to $7.3 billion a year by 2022, up from $2 billion in 2018, according to Juniper Research. This is because companies will invest heavily in AI tools that will help them differentiate and improve the services they offer customers.

In cybersecurity, the adoption of AI brings a boom in startups that are able to raised$3.65 billion in equity funding in the last five years. Cyber AI can help security experts sort through millions of incidents to identify aberrations, risks, and signals of future threats.

And there is even an opportunity brewing in industries facing labour shortages, such as transportation. At the end of 2017, there was a shortage of 51,000 truck drivers (up from a shortage of 36,000 the previous year). And the ATA reports that the trucking industry will need to hire 900,000 more drivers in the next 10 years to keep up with demand. AI-driven autonomous vehicles could help relieve the need for more drivers in the future.

Programming Language

The practice of data science requires the use of analytics tools, technologies and programming languages to help data professionals extract insights and value from data. A recent survey of nearly 24,000 data professionals by Kaggle suggests that Python, SQL and R are the most popular programming languages. The most popular, by far, was Python (83%). Additionally, 3 out of 4 data professionals recommended that aspiring data scientists learn Python first.

Survey results show that 3 out of 4 data professionals would recommend Python as the programming language aspiring data scientists to learn first. The remaining programming languages are recommended at a significantly lower rate (R recommended by 12% of respondents; SQL by 5% of respondents. Anyhow, Python will also boom more in 2019. But, R community too have come up with a lot of recent advancements. With new packages and improvements, R is expected to come closer to python in terms of usage.

Blockchain and Big Data

In recent years, the blockchain is at the heart of computer technologies. It is a cryptographically secure distributed database technology for storing and transmitting information. The main advantage of the blockchain is that it is decentralized. In fact, no one controls the data entering or their integrity. However, these checks run through various computers on the network. These different machines hold the same information. In fact, faulty data on one computer cannot enter the chain because it will not match the equivalent data held by the other machines. To put it simply, as long as the network exists, the information remains in the same state.

Big Data analytics will be essential for tracking transactions and enabling businesses that use the Blockchain to make better decisions. That’s why new Data Intelligence services are emerging to help financial institutions and governments and other businesses discover who they interact with within the Blockchain and discover hidden patterns.

Augmented-Reality/Virtual Reality

The broader the canvas of visualization is, the better the understanding is. That’s exactly what happens when one visualizes big data through the Augmented Reality (AR) and Virtual Reality (VR). A combination of AR and VR could open a world of possibilities to better utilize the data at hand. VR and AR can practically improve the way we perceive data and could actually be the solution to make use of the large unused data.

By presenting the data in the form of 3D, the user will be able to decipher the major takeaways from the data better and faster with easier understanding. Many recent types of research show that the VR and AR has a high sensory impact which promotes faster learning and understanding.

This immersive way of representation of the data enables the analysts to handle the big data more efficiently. It makes the analysis and interpretation more of an experience and realisation that the traditional analysis. Instead of the user seeing numbers and figures, the person will be able to see beyond it and into the facts, happenings and reasons which could revolutionize the businesses.

Edge Computing

Computing infrastructure is an ever-changing landscape of technol­ogy advancements. Current changes affect the way companies deploy smart manufacturing systems to make the most of advancements.

The rise of edge computing capabilities coupled with tradi­tional industrial control system (ICS) architectures provides increasing levels of flexibility. In addition, time-synchronized applications and analytics augment the need for larger Big Data operations in the cloud. This is regardless of cloud premise.

Edge is still in early stage adoption. But, one thing is clear that edge devices are subject to large-scale investments from cloud suppliers to offload bandwidth. Also, there are latency issues due to an explosion of the IoT data in both industrial and commercial applications.

Edge soon will likely increase in adoption where users have questions about the cloud’s specific use case. Cloud-level interfaces and apps will migrate to the edge. Industrial application hosting and analytics will become common at the edge. This will happen using virtual servers and simplified operational technology-friendly hardware and software.

The Rise of Semi-Automated Tools for Data Science

There has been a rise of self-service BI tools such as Tableau, Qlik Sense, Power BI, and Domo. Furthermore, now managers can obtain current business information in graphical form on demand. Although, IT may need to set up a certain amount of setup at the outset. Also, when adding a data source, most of the data cleaning work and analysis can be done by analysts. The analyses can update automatically from the latest data any time they are opened.

Managers can then interact with the analyses graphically to identify issues that need to be addressed. In a BI-generated dashboard or “story” about sales numbers, that might mean drilling down to find underperforming stores, salespeople, and products, or discovering trends in year-over-year same-store comparisons. These discoveries might in turn guide decisions about future stocking levels, product sales and promotions. Also, they may determine the building of additional stores in under-served areas.

Upgrade in Job Roles

In recent times, there have been a lot of advancements in the data science industry. With these advancements, different businesses are in better shape to extract much more value out of their data. With an increase in expectation, there is a shift in the roles of both data scientists and business analysts now. The data scientists should move from statistical focus phase to more of a research phase. But the business analysts are now filling in the gap left by data scientists and are taking their roles up.

We can see it as an upgrade in both the job roles. Business analysts now hold the business angle firm but are also handling the statistical and technical part of the things too. Business analysts are now more into predictive analytics. They are at a stage now where they can use off-the-shelf algorithms for predictions in their business domains. BA’s are not only for reporting and business mindset but now are more into the prescriptive analytics too. They are handling the role of model building, data warehousing and statistical analysing.

Summary

How this question is answered will be fascinating to watch. It could be that the data science field has to completely overhaul what it can offer, overcoming seeming off-limit barriers. Alternatively, it could be that businesses discover their expectations can’t be met and have to adjust to this reality in a productive manner rather than get bogged down in frustration.

In conclusion, 2019 promises to be a year where smart systems make further inroads into our personal and professional lives. More importantly, I expect our professional lives to get more sophisticated with a variety of agents and systems helping us get more of out of our time in the office!

Follow this link, if you are looking to learn more about data science online!

You can follow this link for our Big Data course!

Additionally, if you are having an interest in learning Data Science, click here to start

Furthermore, if you want to read more about data science, you can read our blogs here

Also, the following are some blogs you may like to read

Big Data and Blockchain

What is Predictive Model Performance Evaluation

AI and intelligent applications

 

The Rise of Edge Computing

The Rise of Edge Computing

Introduction

Computing infrastructure is an ever-changing landscape of technol­ogy advancements. Current changes affect the way companies deploy smart manufacturing systems to make the most of advancements.

The rise of edge computing capabilities coupled with tradi­tional industrial control system (ICS) architectures provides increasing levels of flexibility. In addition, time-synchronized applications and analytics augment, or in some cases minimize, the need for larger Big Data operations in the cloud, regardless of cloud premise.

In this blog, we will start with the definition of edge computing. After that, we will discuss the need of edge computing and it’s applications. Also, we will try to understand the scope of edge computing in the future.

What is Edge computing

Consolidation and the centralized nature of cloud computing have proven cost-effective and flexible, but the rise of the IIoT and mobile computing has put a strain on networking band­width. Ultimately, not all smart devices need to use cloud comput­ing to operate. In some cases, architects can — and should — avoid the back and forth. Edge computing could prove more efficient in some areas where cloud computing operates.

Furthermore, edge computing permits data processing closer to it’s origin (i.e., motors, pumps, generators or other sensors), reducing the need to transfer that data back and forth between the cloud.

Additionally, think of edge computing in manufacturing as a network of mi­cro data centers capable of hosting, storage, computing and analysis on a localized basis while pushing aggregate data to a centralized plant or enterprise data center, or even the cloud (private or public, on-premise or off) for further analysis, deeper learning, or to feed an artificial intelligence (AI) engine hosted elsewhere.

According to Microsoft, in edge computing, compute resources are “placed closer to information-generation sources to reduce network latency and bandwidth usage generally associated with cloud computing.” This helps to ensure continuity of services and operations even if cloud connections aren’t steady.

Also, this moving of compute and storage to the “edge” of the network, away from the data centre and closer to the user, cuts down the amount of time it takes to exchange messages compared with traditional centralized cloud computing. Moreover, according to research by IEEE, it can help to balance network traffic, extend the life of IoT devices and, ultimately, reduce “response times for real-time IoT applications.”

Terms in Edge Computing

Like most technology areas, edge computing has its own lexicon. Here are brief definitions of some of the more commonly used terms

  • Edge devices: These can be any device that produces data. These could be sensors, industrial machines or other devices that produce or collect data.
  • Edge: What the edge depends on the use case. In a telecommunications field, perhaps the edge is a cell phone or maybe it’s a cell tower. Furthermore, in an automotive scenario, the edge of the network could be a car. Also, in manufacturing, it could be a machine on a shop floor. Additionally, in enterprise IT, the edge could be a laptop.
  • Edge gateway: A gateway is a buffer between where edge computing processing is done and the broader fog network. The gateway is the window into the larger environment beyond the edge of the network.
  • Fat client: Software that can do some data processing in edge devices. This is opposite to a thin client, which would merely transfer data.
  • Edge computing equipment: Edge computing uses a range of existing and new equipment. We can outfit many devices, sensors and machines to work in an edge computing environment by simply making them Internet-accessible. Cisco and other hardware vendors have a line of rugged network equipment that has hardened exteriors meant to be used in field environments. A range of compute servers and even storage-based hardware systems like Amazon Web Service’s Snowball have usage in edge computing deployments.
  • Mobile edge computing: This refers to the buildout of edge computing systems in telecommunications systems, particularly 5G scenarios

Why Rise in Edge Computing

1. Latency in decision making

Businesses are getting a huge boost from computerised systems, especially as they evolve into the cloud era. But bringing that same level of technology across different sites has proven to be not so straightforward for many companies, particularly as the sites started generating more data. The main concern is latency, that being the time it takes for data to move between points. As with the NYSE, a little distance goes a long way in the computer world, so it stands to reason that delays in sending data needed to reach decisions will translate into delays for the business.

2. Decentralisation and scaling

To some, it may seem counterintuitive to move away from the centre. Wasn’t centralisation the whole point of cloud systems? But the cloud isn’t about pooling everything in the middle. It’s about scale and making it easier to access the services that the business uses every day. Also, the transfer gap problem between sites and data centres predates the cloud era. Yet cloud can exacerbate it. The only way to overcome this transfer gap is to move some of the data centres to where the data is.

3. Process Optimisation

With edge computing, data centres can execute rules that are time sensitive (like “stop the car” in case of driverless vehicles), and then stream data to the cloud in batches when bandwidth needs aren’t as high. Furthermore,the cloud can then take the time to analyze data from the edge, and send back recommended rule changes — like “decelerate slowly when the car senses human activity within 50 feet.”

4. Cost

Cost is also a driving factor for edge computing. The bulk of telemetry data that is from the sensors and actuators is likely not relevant for the IoT application. The fact a temperature sensor reports a 20ºC reading every second might not be interesting until the sensor reports a 40ºC reading. Edge computing allows for the filtering and processing of data before sending it to the cloud. This reduces the network cost of data transmission. It also reduces the cloud storage and processing cost of data that is not relevant to the application.

5. Resourcefulness

Storing and processing data on the edge and only sending out to the cloud what will be used and useful saves bandwidth and server space.

Where all we are using it

1. Grid Edge Control and Analytics

Grid Edge computing solutions are helping the utility monitor and analyse these additional renewable power generating resources integrated into their grid, in real time. This is something legacy SCADA systems are unable to offer.

From residential rooftop solar to solar farms, commercial solar, electric vehicles and wind farms, smart meters are generating a ton of data that helps utilities to view the amount of energy available and required, allowing their demand response to become more efficient, avoid peaks and reduce costs. This data is first processed in the Grid Edge Controllers that perform local computation and analysis of the data only send necessary actionable information over a wireless network to the Utility.

2. Oil and Gas Remote Monitoring

Safety monitoring within critical infrastructures such as oil and gas utilities is of utmost importance. For this reason, many cutting edge IoT monitoring devices are being deployed in order to safeguard against disaster. Edge computing allows data to be analysed, processed, and then delivered to end-users in real-time, allowing for control centres to access data as it occurs in order to foresee and prevent malfunctions or incidents before they occur. This is really important. As, when dealing with critical infrastructures such as oil and gas or other energy services, any failures within a particular system have the potential to be catastrophic and should always warrant the highest levels of precaution.

3. Internet of Things

A smart window firm monitors windows for errors, weather information, maintenance needs and performance. This generates a massive stream of data as each device is regularly reporting information. Edge services filter this information and report a summary back to a centralized service that is running from the firm’s primary data centres. By summarizing information before reporting it, global bandwidth consumption is reduced by 99%.

4. E-Commerce

An e-commerce company delivers images and static web content from a content delivery network. They also perform processing at edge data centres to quickly calculate product recommendations for customers.

5. Markets

A hedge fund pays an expensive premium for servers that are in close proximity to various stock exchanges to achieve extremely low latency trading. Trading algorithms are deployed on these machines. These servers are expensive and resource constrained. As such, they connect back to a cloud service for processing support.

6. Games

A game platform executes certain real-time elements of the game experience on edge servers near the user. The edges connect to a cloud backend for support processing. The backend is run from three regions that need not be close to the end-user.

Predictions for Edge Computing in Future

According to IDC  by 2020, the IT spend on edge infrastructure will reach up to 18% of the total spend on IoT infrastructure. That spend is driven by the deployment of converged IT and OT systems which reduces the time to value of data collected from their connected devices IDC adds. It’s what we explained and illustrated in a nutshell.

According to a November 1, 2017, announcement regarding research of the edge computing market across hardware, platforms, solutions and applications (smart city, augmented reality, analytics etc.) the global edge computing market is expected to reach USD 6.72 billion by 2022 at a compound annual growth rate of a whopping 35.4 per cent.

The major trends responsible for the growth of the market in North America are all too familiar. Also, there is a growing number of devices and dependency on IoT devices. Hence, the need for faster processing, the increase in cloud adoption, and the increase in pressure on networks.

In an October 2018 blog post, Gartner’s Rob van der Meulen said that currently, around 10% of enterprise-generated data is created and processed outside a traditional centralized data centre or cloud. By 2022, Gartner predicts this figure will reach 50 per cent.

Summary

Edge is still in early stage adoption, but one thing is clear: Edge devices are subject to large-scale investments from cloud suppliers to offload bandwidth. Also, there are latency issues due to an explosion of the Internet of Things (IoT) data in both industrial and commercial applications.

Edge soon will likely increase in adoption where users have questions about how or if the cloud applies for the specific use case. Cloud-level interfaces and apps will migrate to the edge. Industrial application hosting and analytics will become common at the edge, using virtual servers and simplified operational technology-friendly hardware and software.

Benefits in network simplification, security and bandwidth accompany the IT simplification.

Follow this link, if you are looking to learn more about data science online!

You can follow this link for our Big Data course!

Additionally, if you are having an interest in learning Data Science, click here to start

Furthermore, if you want to read more about data science, you can read our blogs here

Also, the following are some blogs you may like to read

MATLAB for Data Science

Top 5 Ways to Evaluate Data Science Competency

Can you learn Data Science and Machine Learning without Maths?